The occurrence and proliferation of pests can seriously affect the productivity and quality of agricultural products. Without monitoring the number and status of the pests, farmers cannot control and timely detect crop damage or disease. Therefore, monitoring the number and status of the pests in farming areas is an urgent task for the development of agriculture.
At present, most of the methods used for detecting the pests are conducted through labors regularly investigating the number of pests in the field. When calculating the number of the pests captured, it is necessary to calculate the number of the pests by labors. This way is not only inconvenient and inaccurate but also time and labor consuming. Also, information of the pests cannot be instantly known, instantly reported, and monitored. In addition, the state of the pests is also closely related to the environment and climate. If information of the environment and the climate is manually recorded, it cannot be timely and effectively integrated with information of the pests. Therefore, how to establish a pest surveillance system that can integrate environmental parameters and can automatically calculate the number of the pests quickly and accurately is a focus for relevant people in the field.